Prompt Engineering Best Practices
Get introduced to the best practices of prompt engineering.
Overview#
Best practices are established methods or techniques recognized as the most effective and efficient ways to achieve a particular goal or outcome. Examples of best practices can include procedures, protocols, guidelines, and methodologies that have successfully achieved specific goals or objectives. They are widely accepted as the most effective way of doing things and are essential for achieving optimal results.
The principles#
Let's talk about the principles of prompt engineering, including examples, as they provide guidelines for creating effective prompts that ensure accurate results.
Simplicity #
Simplicity is an essential factor to consider when crafting prompts for natural language processing models. The prompts should be concise, clear, and easy to understand for both the model and the end user. Using overly complex language or providing unnecessary information can confuse the model and lead to inaccurate results.
For example, the following prompt may be too wordy and convoluted for the model to accurately understand and generate the desired output.
Considering the input factors of the user's geolocation, flavored food preferences, and budgetary
restrictions, please generate a list of restaurant recommendations for the individual in question.
In contrast, the following prompt is simple and contains only the necessary information to guide the model toward the desired output:
Using the following parameters, generate a list of recommended restaurants based on the user's
location, cuisine preference, and price range.
Specificity #
Specificity is an essential aspect of prompt engineering in natural language processing, as it ensures that the generated output is relevant and accurate. When crafting prompts, it is essential to be specific about the desired output, task, or objective. Generic prompts may not guide the model enough to generate accurate results.
For example, the following prompt is too general and could result in a wide range of output descriptions that may not be relevant to the user's needs.
Generate a description of a dog.
However, consider a more specific prompt that provides clear guidance to the model and helps ensure the generated output is relevant and accurate.
Generate a description of a golden retriever with a curly tail, a friendly personality, and who
loves to play fetch.
By providing specific details in the prompt, we can help the model to focus on the relevant aspects of the task and improve the accuracy of its results.
Essential prompt keywords#
Essential prompt keywords are specific words or phrases that convey the intended meaning and guide the natural language processing model toward generating the desired output. Including relevant keywords in prompts ensures the model understands the task or objective and produces accurate results.
For example, consider a prompt like “Summarize the main points of a news article about climate change.” In this prompt, the essential keywords are “summarize,” “news article,” and “climate change.” These keywords guide the model on what task to perform, what type of input data to expect, and what topic to focus on.
Other examples of essential prompt keywords include verbs that specify the desired action, such as “generate,” “classify,” or “translate,” as well as specific nouns that describe the input data, such as “image,” “text,” or “audio.” Including essential prompt keywords helps to ensure that the natural language processing model produces accurate and relevant results that meet the user's needs. Here's a list of essential prompt keywords:
What can go wrong while prompting#
Several factors can affect the accuracy and relevance of the results generated by natural language processing models when prompted. Can you identify why the following prompts are wrong?
Write about the benefits of using
social media
Ambiguity
Prove that climate change is a hoax
Insufficient context
What is the best restaurant in town?
Bias
Write a story about a girl named
Sarah who goes on a picnic
Too specific
Citing references is a crucial aspect of many types of writing, including academic and scientific publications. However, large language models can sometimes fail to provide proper attribution, leading to issues with accuracy and credibility. For example, a language model may generate the following sentence:
Output |
According to recent research, a new treatment for cancer has been discovered that has a 100% success rate. |
However, if the model fails to provide a reference or citation for the research, it may be difficult or impossible for the reader to verify the claim. This can be especially problematic in scientific writing, where accurate and reliable information is crucial.
Solving math problems#
Solving math problems is another area where large language models can fall short. While these models are excellent at generating text, they are not designed to handle complex mathematical equations or operations. For example, if asked to solve the following equation:
Prompt |
Solve: 2x + 3 = 7 |
A language model may be able to perform basic arithmetic and generate the answer, x=2 , but it would likely struggle with more complex equations that require knowledge of advanced mathematical concepts.
Hallucination#
Large language models can sometimes generate outputs that are not grounded in reality. Hallucination can occur when the model generates text based on incomplete or incorrect information. For example, a language model may generate the following sentence:
Output |
The ground is made up from clouds. |
While this statement is false, this type of output can be problematic in applications such as news generation or chatbots, where false or misleading information can have serious consequences.
In conclusion, while large language models are impressive and powerful tools, it's essential to be aware of their limitations. Careful consideration and appropriate use of these models can help mitigate these limitations and maximize their potential benefits.
Types of Prompts
Role Prompting